5.3 THE SUMMARY EFFECT SIZE AND HETEROGENEITY ANALYSIS

The analysis of data was undertaken in accordance with the methodology described in Chapter-4. The reported effect sizes were first adjusted using the procedures devised under the weighting scheme. These adjustments were performed manually, as due to the uniqueness of the weighting scheme, none of the available software packages enabled such computations. However, the summary effect size computations, and sub-group analyses for moderators, were performed using CMA. The data analysis and findings are presented next, commencing with the summary effect size and related findings. The subgroup analyses and findings for hypothesised moderation effects are presented subsequently.

Study Industry type Firm size Technological Turbulence

Sirén et al. (2012) Service Mixed High Tatikonda and Montoya-Weiss

(2001)

Manufacturing Mixed High

Thornhill (2006) Manufacturing SMEs High Vorhies and Morgan (2005) Mixed Mixed High Wolff and Pett (2006) Mixed SMEs High Yalcinkaya et al. (2007) Manufacturing Mixed High Yam et al. (2004) Mixed Mixed Low Yam et al. (2011) Manufacturing Mixed High Zahra and Bogner (2000) Service SMEs High Zahra and Covin (1993) Manufacturing NA High

Page | 117

5.3.1. Summary effect size and associated results

As outlined in Section-4.6, CMA does not compute the summary effect size directly from correlations but rather, transforms the correlations into their equivalent Fisher‘s z-coefficients. This procedure conforms to the commonly used Hedges and Olkin (1985) approach. Z-coefficients are averaged after they are weighted by an estimate of the inverse of their variance and reconverted back into a summary correlation, which is in the same metric as

the reported correlations. The Run Analyses command on the data-entry

interface of CMA was executed to obtain the summary effect size, Confidence Intervals and forest plots, as presented in Table 5.3. A Confidence Interval (CI) indicates the ―precision with which the effect size has been estimated in that study‖ (Borenstein et al., 2009: 5), and 95% level is commonly used as an appropriate degree of precision. Therefore, a CI signifies the degree to which the summary effect size can be relied upon. The summary effect size results are for the RE model, which was deemed appropriate for the current study (see Section-4.2.1.).

The summary effect size for the relationship between PIC and firm performance was found to be 0.379 (p < 0.05). This constitutes a core finding of the current study. The summary effect size value of 0.379 represents the magnitude and direction of the relationship of interest. Judging by the heuristics proposed by Cohen (1977), the values of 0.10, 0.30 and 0.50 can be considered small, medium and large respectively; the summary effect size value obtained for the relationship can thus be considered moderately-large in magnitude. This suggests that PIC and firm performance are strongly related constructs. In Table 5.3, the columns labelled—Statistics for each study and, Correlation and 95 percent CI, present the numerical and visual forms of the summary correlation and CI. The labels—Favours A and Favours B, at the bottom of the forest plot are extraneous for the current study as they relate to experimental study designs that often use randomised controlled trials (e.g., see Chan et al., 2007). Also, as mentioned in the Table of Matrices, some compatibility issues between CMA and MS Word caused the Tables imported from CMA to not display optimally, but they are used for their scientific validity.

Page | 118 Table 5.3.: The summary effect size and CI

As can be seen in the Table, the CI95% (i.e., the CI corresponding to a 95%

confidence in the degree of precision) of the summary effect size ranged from a low of 0.305 to a high of 0.448. Hence, it can be concluded that the summary effect size of 0.379 is estimated with a high degree of precision. As

the p-value is significant, and the CI does not encompass the value of zero

(see Table 5.3), the possibility of a Null relationship between PIC and firm

performance is dismissed and it is concluded that there is a significantly positive relationship between the two variables. Therefore, the first Hypothesis (H1) is supported.

The Table 5.4 presents the summary effect size and the corresponding CIs, both numerically and visually (via forest plots). Forest plots are extremely useful for visual interpretations and assessments of meta-analytic statistics, in addition to highlighting any problems with the dataset (Borenstein et al., 2009). ―The forest plot is a compelling piece of information and easy to understand‖ (Borenstein et al., 2009: 366), and is therefore presented wherever applicable in this Chapter.

Study name Statistics for each study Correlation and 95% CI

Lower Upper

Correlation limit limit Z-Value p-Value

0.379 0.305 0.448 9.331 0.000

-1.00 -0.50 0.00 0.50 1.00

Favours A Favours B

Summary effect-size and CI

Page | 119 Table 5.4: Statistics for individual studies and forest plot

Study name Statistics for each study Correlation and 95% CI

Lower Upper Relative Relative

Correlation limit limit Z-Value p-Value weight weight

Akgun et al (2009) 0.636 0.535 0.719 9.505 0.000 1.73

Ar and Baki (2011) 0.312 0.200 0.416 5.274 0.000 1.77

Artz et al (2010) 0.044 -0.075 0.162 0.722 0.470 1.77

Baer and Frese (2003) 0.142 -0.151 0.412 0.948 0.343 1.50

Calantone et al (2002) 0.481 0.363 0.584 7.112 0.000 1.74

Chen, Lin and Chang (2009) 0.591 0.451 0.703 6.893 0.000 1.68

Chen, Tsou and Huang (2009) 0.653 0.538 0.744 8.550 0.000 1.70

Coombs and Bierly (2006) 0.136 -0.002 0.269 1.926 0.054 1.75

Correa et al (2007) 0.493 0.416 0.563 10.868 0.000 1.79

Craig and Dibrell (2006) 0.734 0.682 0.778 17.711 0.000 1.79

Cui et al (2005) 0.561 0.431 0.668 7.176 0.000 1.71

Dai and Liu (2009) 0.334 0.267 0.398 9.242 0.000 1.81

Deeds et al (1998) 0.291 0.088 0.471 2.779 0.005 1.65

Dibrell et al (2008) 0.290 0.197 0.378 5.926 0.000 1.79

Eisingerich et al (2009) 0.779 0.695 0.842 10.987 0.000 1.69

Ettlie and Pavlou (2006) 0.247 -0.050 0.504 1.635 0.102 1.48

Garcia- Morales et al (2007) 0.619 0.535 0.691 11.276 0.000 1.77 Garg et al (2003) 0.014 -0.178 0.205 0.141 0.888 1.67 Gopalakrishnan (2000) 0.250 0.057 0.425 2.528 0.011 1.67 Grawe et al (2009) 0.397 0.298 0.488 7.288 0.000 1.78 Guan and Ma (2003) 0.123 -0.012 0.253 1.792 0.073 1.76 Heeley et al (2007) 0.084 0.032 0.136 3.162 0.002 1.83 Heunks (1998) 0.196 0.059 0.326 2.787 0.005 1.75 Hult et al (2004) 0.571 0.464 0.662 8.659 0.000 1.74 Jansen et al (2006) 0.233 0.120 0.340 3.972 0.000 1.78

Jimenez and Valle (2011) 0.519 0.448 0.583 12.170 0.000 1.80

Kalafsky and MacPherson (2002) 0.428 0.257 0.573 4.597 0.000 1.67

Katila and Ahuja (2002) -0.007 -0.183 0.170 -0.077 0.939 1.70

Kim et al (2011) 0.222 0.066 0.367 2.774 0.006 1.72

Kuckertz et al (2010) 0.337 0.052 0.571 2.300 0.021 1.49

Lawson et al (2012) 0.393 0.280 0.495 6.367 0.000 1.76

Lee et al (2001) 0.566 0.440 0.670 7.427 0.000 1.71

Li and Atuahene-Gima (2001) 0.531 0.418 0.627 7.958 0.000 1.74

Lin and Chen (2008) 0.442 0.335 0.538 7.385 0.000 1.77

Loof and Heshmati (2006)- Sample A 0.462 0.370 0.545 8.815 0.000 1.78

Loof and Heshmati (2006)- Sample B 0.224 0.159 0.287 6.584 0.000 1.82

Luo et al (2005) 0.224 0.098 0.343 3.456 0.001 1.76

Mithas et al (2011) 0.933 0.910 0.951 21.064 0.000 1.73

O'Cass and Sok (2012) 0.669 0.572 0.747 10.039 0.000 1.73

O'Cass and Sok (2013) 0.872 0.831 0.904 17.386 0.000 1.74

Panayides (2006) 0.494 0.394 0.582 8.525 0.000 1.77

Penner-Hahn and Shaver (2005) -0.132 -0.364 0.116 -1.045 0.296 1.58

Rhodes et al (2008) 0.670 0.591 0.737 12.025 0.000 1.76

Richard et al (2003) -0.369 -0.490 -0.234 -5.108 0.000 1.74

Richard et al (2004) 0.211 0.054 0.358 2.624 0.009 1.72

Salomo et al (2008) 0.291 0.119 0.446 3.269 0.001 1.70

Schilke (2014) 0.346 0.238 0.445 5.996 0.000 1.77

Schoenecker and Swanson (2002) -0.077 -0.281 0.133 -0.715 0.474 1.65

Siren et al (2012) 0.234 0.101 0.359 3.397 0.001 1.75

Tatikonda and Montoya- Weiss (2001) 0.414 0.254 0.552 4.764 0.000 1.69

Thornhill (2006) -0.029 -0.096 0.038 -0.847 0.397 1.82

Vorhies and Morgan (2005) 0.303 0.181 0.416 4.713 0.000 1.76

Wolff and Pett (2006) 0.168 0.023 0.306 2.269 0.023 1.74

Yalcinkaya et al (2007) 0.214 0.029 0.385 2.259 0.024 1.68

Yam et al (2004) 0.153 0.019 0.282 2.235 0.025 1.76

Yam et al (2011) 0.314 0.183 0.434 4.561 0.000 1.75

Zahra and Bogner (2000) 0.256 0.077 0.419 2.783 0.005 1.69

Zahra and Covin (1993) 0.360 0.179 0.517 3.769 0.000 1.67

0.379 0.305 0.448 9.331 0.000

-1.00 -0.50 0.00 0.50 1.00 Favours A Favours B